Chapman and Hall/CRC
Considered highly exotic tools as recently as the late 1990s, microarrays are now ubiquitous in biological research. Traditional statistical approaches to design and analysis were not developed to handle the high-dimensional, small sample problems posed by microarrays. In just a few short years the number of statistical papers providing approaches to analyzing microarray data has gone from almost none to hundreds if not thousands. This overwhelming deluge is quite daunting to either the applied investigator looking for methodologies or the methodologist trying to keep up with the field. DNA Microarrays and Related Genomics Techniques: Design, Analysis, and Interpretation of Experiments consolidates discussions of methodological advances into a single volume.
The book’s structure parallels the steps an investigator or an analyst takes when conducting and analyzing a microarray experiment from conception to interpretation. It begins with foundational issues such as ensuring the quality and integrity of the data and assessing the validity of the statistical models employed, then moves on to cover critical aspects of designing a microarray experiment. The book includes discussions of power and sample size, where only very recently have developments allowed such calculations in a high dimensional context, followed by several chapters covering the analysis of microarray data. The amount of space devoted to this topic reflects both the variety of topics and the effort investigators have devoted to developing new methodologies. In closing, the book explores the intellectual frontier – interpretation of microarray data. It discusses new methods for facilitating and affecting formalization of the interpretation process and the movement to make large high dimensional datasets public for further analysis, and methods for doing so.
There is no question that this field will continue to advance rapidly and some of the specific methodologies discussed in this book wil
Microarray Platforms and Blood Samples. Normalization of Microarray Data. Microarray Quality Control and Assessment. Epistemological Foundations of Statistical Methods for High-Dimensional Biology. The Role of Sample Size on Measures of Uncertainty and Power. Pooling Biological Samples in Microarray Experiments. Designing Microarrays for the Analysis of Gene Expressions. Overview of Standard Clustering Approaches for Gene Microarray Data Analysis. Cluster Stability. Dimensionality Reduction and Discrimination. Modeling Affymetrix Data at the Probe Level. Parametric Linear Models. The Use of Nonparametric Procedures in the Statistical Analysis of Microarray Data. Bayesian Analysis of Microarray Data. False Discovery Rate and Multiple Comparison Procedures. Using Standards to Facilitate Interoperation of Heterogeneous Microarray Databases and Analytic Tools. Post-Analysis Interpretation: What Do I Do with This Gene List? Combining High Dimensional Biological Data to Study Complex Diseases and Quantitative Traits.